Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery

Amr Abd-Elrahman,Cheryl Dalid,Benjamin Wilkinson,Ali Gonzalez,Katherine Britt,Zhen Guan,Vance Whitaker

doi:10.3390/rs12213632

Abstract

Capturing high spatial resolution imagery is becoming a standard operation in many agricultural applications. The increased capacity for image capture necessitates corresponding advances in analysis algorithms. This study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis and utilize the extracted metrics in statistical modeling of strawberry dry weight. Automated canopy delineation and canopy size metrics extraction models were developed and implemented using ArcMap software v 10.7 and made available by the authors. The workflows were demonstrated using high spatial resolution (1 mm resolution) orthoimages and digital surface models (2 mm) of 34 strawberry plots (each containing 17 different plant genotypes) planted on raised beds. The images were captured on a weekly basis throughout the strawberry growing season (16 weeks) between early November and late February. The results of extracting four canopy size metrics (area, volume, average height, and height standard deviation) using automatically delineated and visually interpreted canopies were compared. The trends observed in the differences between canopy metrics extracted using the automatically delineated and visually interpreted canopies showed no significant differences. The R2 values of the models were 0.77 and 0.76 for the two datasets and the leave-one-out (LOO) cross validation root mean square error (RMSE) of the two models were 9.2 g and 9.4 g, respectively. The results show the feasibility of using automated methods for canopy delineation and canopy metric extraction to support plant phenotyping applications.

Highlights

The use of high-resolution remote sensing technologies established deep roots in the natural resource and agricultural management fields [1,2]
Many remote sensing applications focus on utilizing the spectral information in the images using spectral indices, for example, with less emphasis on geometrical canopy characteristics that can be extracted from the images
Automated canopy delineation workflow required a point within each canopy close to the highest point of the canopy

Summary

Introduction

The use of high-resolution remote sensing technologies established deep roots in the natural resource and agricultural management fields [1,2]. Interactive analysis of remote sensing data can be beneficial for data exploration; the current flood of remote sensing datasets mandates the development of automated analysis methods that are efficient, adaptable, and easy to use. This is especially important to reduce the cost and increase the benefit of using these technologies and to efficiently apply them to small farms and research experiments that are typical for specialty crops such as strawberries. Spectral information has been used to infer vegetation biophysical parameters such as leaf area and biomass, which are related to canopy sizes and structure, and are necessary for ecological and climate change models [16]

Methods

Results

Discussion

Conclusion